US20140213862A1 - Wavelet-based system and method for analyzing a physiological signal - Google Patents

Abstract

Certain embodiments of the present disclosure provide a system and method for analyzing a physiological signal detected from an individual. The system may include a physiological signal detection module configured to detect the physiological signal of the individual, a wavelet formation module configured to form a wavelet based on the physiological signal, and a wavelet transform module configured to generate a scalogram by transforming the physiological signal with the wavelet based on the physiological signal.

Description

FIELD
• Embodiments of the present disclosure generally relate to physiological signal processing and, more particularly, to a system and method for analyzing a physiological signal using a wavelet that is derived from, formed from, or otherwise based on the physiological signal.
BACKGROUND
• A wavelet function may be applied to a physiological signal, such as a photoplethysmograph (PPG) signal, to form a scalogram that separates the physiological signal into component parts. The component parts may be analyzed to determine one or more physical parameters, such as pulse rate.
• Typically, the wavelet or wavelet function may be a mathematical function such as a Morlet wavelet, a Mexican hat wavelet, a Hermitian wavelet, a Shannon wavelet, a Beta wavelet, a Paul wavelet, or the like. However, because the wavelet function is not specific to the physiological signal itself, the resulting scalogram may contain undesired information, such as noise and interference, or otherwise be unreliable.
SUMMARY
• Certain embodiments of the present disclosure provide a system for analyzing a physiological signal detected from an individual. The system may include a physiological signal detection module configured to detect the physiological signal of the individual, a wavelet formation module configured to form a wavelet based on the physiological signal, and a wavelet transform module configured to generate a scalogram by transforming the physiological signal with the wavelet based on the physiological signal. The system may also include a physiological parameter determination module configured to determine one or more physiological parameters of the individual through an analysis of the scalogram.
• The system may also include a detection sub-system in communication with the physiological signal detection module. The physiological signal detection module may be configured to receive the physiological signal from the detection sub-system. The detection sub-system may include a photoplethysmograph (PPG) sub-system, and the physiological signal may include a PPG signal. Alternatively, the detection sub-system may include a blood pressure detection sub-system, and the physiological signal may include a blood pressure signal. In another embodiment, the detection sub-system may include an electrocardiogram (EKG) sub-system, and the physiological signal may include an EKG signal.
• In at least one embodiment, the wavelet formation module may be configured to form the wavelet by directly analyzing the physiological signal and shaping the wavelet to directly correlate to the physiological signal. The wavelet formation module may be configured to contain at least a portion of the physiological signal within a Gaussian window or envelope to form the wavelet. In at least one embodiment, the wavelet may include a derivative of the physiological signal. In another embodiment, the wavelet formation module may be configured to form the wavelet by using one or more shapes that are similar to one or more portions of a generic shape of the physiological signal.
• Certain embodiments of the present disclosure provide a method of analyzing a physiological signal detected from an individual. The method may include detecting a physiological signal of the individual with a physiological signal detection module, forming a wavelet based on the physiological signal with a wavelet formation module, and generating a scalogram with a wavelet transform module by transforming the physiological signal with the wavelet based on the physiological signal.
• Certain embodiments of the present disclosure provide a tangible and non-transitory computer readable medium that includes one or more sets of instructions configured to direct a computer to detect a physiological signal of an individual, form a wavelet based on the physiological signal, and generate a scalogram by transforming the physiological signal with the wavelet based on the physiological signal.
BRIEF DESCRIPTION OF THE DRAWINGS
• FIG. 1 illustrates a simplified block diagram of a system for determining a physiological signal, according to an embodiment of the present disclosure.
• FIG. 2(a) illustrates a top plan view of a scalogram derived from a physiological signal, according to an embodiment of the present disclosure.
• FIG. 2(b) illustrates an isometric top view of a scalogram derived from a physiological signal, according to an embodiment of the present disclosure.
• FIG. 2(c) illustrates an exemplary scalogram derived from a signal containing two pertinent components, according to an embodiment of the present disclosure.
• FIG. 2(d) illustrates a schematic of signals associated with a ridge inFIG. 2(c), and a schematic of a further wavelet decomposition of the signals, according to an embodiment of the present disclosure.
• FIG. 3 illustrates a Morlet wavelet.
• FIG. 4 illustrates a photoplethysmograph (PPG) signal over time, according to an embodiment of the present disclosure.
• FIG. 5 illustrates a top plan view of a scalogram formed by the Morlet wavelet shown inFIG. 3 transforming the PPG signal shown inFIG. 4.
• FIG. 6 illustrates an isometric view of a scalogram formed by the Morlet wavelet shown inFIG. 3 transforming the PPG signal shown inFIG. 4.
• FIG. 7 illustrates a synthesized wavelet, according to an embodiment of the present disclosure.
• FIG. 8 illustrates a top plan view of a scalogram formed by the synthesized wavelet shown inFIG. 7 transforming the PPG signal shown inFIG. 4, according to an embodiment of the present disclosure.
• FIG. 9 illustrates an isometric view of a scalogram formed by the synthesized wavelet shown inFIG. 7 transforming the PPG signal shown inFIG. 4, according to an embodiment of the present disclosure.
• FIG. 10 illustrates a simplified PPG signal over time, according to an embodiment of the present disclosure.
• FIG. 11 illustrates a series of PPG signals within a Gaussian window, according to an embodiment of the present disclosure.
• FIG. 12 illustrates a PPG wavelet, according to an embodiment of the present disclosure.
• FIG. 13 illustrates a top plan view of a scalogram formed by the PPG wavelet shown inFIG. 12 transforming the PPG signal shown inFIG. 4, according to an embodiment of the present disclosure.
• FIG. 14 illustrates an isometric view of a scalogram formed by the PPG wavelet shown inFIG. 12 transforming the PPG signal shown inFIG. 4, according to an embodiment of the present disclosure.
• FIG. 15 illustrates a PPG wavelet derived from a derivative of the PPG signal shown inFIG. 4, according to an embodiment of the present disclosure.
• FIG. 16 illustrates an isometric view of a photoplethysmogram system, according to an embodiment of the present disclosure.
• FIG. 17 illustrates a simplified block diagram of a PPG system, according to an embodiment of the present disclosure.
• FIG. 18 illustrates a blood pressure signal over time, according to an embodiment of the present disclosure.
• FIG. 19 illustrates an echocardiograph (EKG) signal over time, according to an embodiment of the present disclosure.
• FIG. 20 illustrates a flow chart of a method of determining one or more physiological parameters, according to an embodiment of the present disclosure.
DETAILED DESCRIPTION
• FIG. 1 illustrates a simplified block diagram of asystem100 for determining a physiological signal, according to an embodiment of the present disclosure. Thesystem100 may include a physiologicalsignal detection module102, awavelet formation module104, awavelet transform module106, a physiologicalparameter determination module108, and adisplay110. Thesystem100 is configured to detect a physiological signal, such as a PPG signal, blood pressure signal, electrocardiograph (EKG) signal, or the like, through the physiologicalsignal detection module102, which may be operatively connected to, and/or in communication with a detection sub-system (not shown inFIG. 1), such as pulse oximeter, blood pressure detector, EKG detector, or the like. Thewavelet formation module104 receives the physiological signal, and derives, constructs, determines, forms, or otherwise generates a wavelet based on the physiological signal itself, as opposed to using a random, arbitrary, or constant wavelet, such as a Morlet wavelet. Thewavelet transform module106 then utilizes the wavelet that is based on the physiological signal to form a scalogram, which decouples various components of the physiological signal for analysis. The physiologicalparameter determination module108 then analyzes the scalogram to determine one or more physiological parameters, such as pulse rate, respiratory effort, and/or the like. Because the wavelet is based on the physiological signal itself, the resulting scalogram provides clear, accurate, and reliable information regarding the physiological parameters, as compared to a scalogram formed by using an arbitrary, random, and/or constant wavelet function. Thedisplay110 may show one or more of the physiological signal, the wavelet, the scalogram, and/or the physiological parameters. Themodules102,104,106, and108, and the display may be operatively connected to one another through circuits, cables, wireless connections, and/or the like.
• The physiologicalsignal detection module102 may be configured to receive a physiological signal, such as a PPG or blood pressure signal, from a detection sub-system or device (not shown inFIG. 1) that is configured to detect the physiological signal of an individual. The physiologicalsignal detection module102 may analyze the physiological signal and display it on thedisplay110 as a two-dimensional waveform.
• Thewavelet formation module104 analyzes the physiological signal and forms a wavelet based directly on the physiological signal itself. For example, the physiological signal may be analyzed or otherwise used to form the wavelet. The resulting wavelet may also be displayed on thedisplay110. Thewavelet formation module104 continually analyzes the received physiological signal. Therefore, as the physiological signal changes over time, thewavelet formation module104 may alter the wavelet based on the changing physiological signal. Accordingly, the wavelet continually adapts to the morphology of the physiological signal over time.
• Thewavelet transform module106 receives the physiological signal and the formed wavelet from the physiologicalsignal detection module102 and thewavelet formation module104, respectively. Thewavelet transform module106 is configured to transform the physiological signal with the wavelet to yield a scalogram, which may also be displayed on thedisplay110
• The physiologicalparameter determination module108 is configured to analyze the scalogram to determine one or more physiological parameters, such as pulse rate, blood pressure, respiratory effort, and/or the like, which may also be shown on thedisplay110. As such, the physiological parameter(s) is accurate and reliable, as it is determined through use of a wavelet that is based on and directly correlates with the physiological signal itself.
• Thesystem100 may be contained within a workstation that may be or otherwise include one or more computing devices, such as standard computer hardware. Eachmodule102,104,106, and108 may include one or more control units, such as processing devices that may include one or more microprocessors, microcontrollers, integrated circuits, memory, such as read-only and/or random access memory, and the like.
• Themodules102,104,106, and108 may be integrated into a single module and contained within a single housing. Alternatively, eachmodule102,104,106, and108 may be its own separate and distinct module, and contained within a respective housing.
• Thedisplay110 may be a cathode ray tube display, a flat panel display, such as a liquid crystal display (LCD), a light-emitting diode (LED) display, a plasma display, or any other type of monitor. As noted, thesystem100 may be configured to calculate physiological parameters and to show information related to the physiological parameters on thedisplay110.
• Thesystem100 may include any suitable computer-readable media used for data storage. For example, one or more of themodules102,104,106, and108 may include computer-readable media. The computer-readable media are configured to store information that may be interpreted by themodules102,104,106, and108. The information may be data or may take the form of computer-executable instructions, such as software applications, that cause a microprocessor or other such control unit within themodules102,104,106, and108 to perform certain functions and/or computer-implemented methods. The computer-readable media may include computer storage media and communication media. The computer storage media may include volatile and non-volatile media, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. The computer storage media may include, but are not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store desired information and that may be accessed by components of the system.
• FIGS. 2(a) and2(b) illustrate top plan and isometric views, respectively, of a scalogram derived from a physiological signal, according to an embodiment of the present disclosure. A physiological signal, such as a PPG signal, EKG signal, or a blood pressure signal, may be transformed using a wavelet transform. Information derived from the transform of the pressure signal may be used to provide measurements of one or more physiological parameters.
• The wavelet transform of a signal x(t) may be defined as shown in Equation (1):
• $\begin{array}{cc}T\left(a,b\right)=\frac{1}{\sqrt{a}}{\int }_{-\infty }^{+\infty }x\left(t\right){\psi }^{*}\left(\frac{t-b}{a}\right)t& \mathrm{Equation}\left(1\right)\end{array}$
• where ψ*(t) is the complex conjugate of the wavelet function ψ(t), a is the dilation or scale parameter of the wavelet, b is the location parameter of the wavelet, and x(t) is the signal under investigation. For example, x(t) may be a physiological signal, such as a PPG signal, a blood pressure signal, an EKG signal, or the like. The wavelet function ψ(t) may be the wavelet that transforms the physiological signal x(t) into a scalogram. The wavelet function ψ(t) may be formed from or otherwise based on the physiological signal x(t).
• The transform given by Equation (1) may be used to construct a representation of a signal on a transform surface. The transform may be regarded as a time-scale representation. Wavelets are composed of a range of frequencies, one of which may be denoted as the characteristic frequency of the wavelet, where the characteristic frequency associated with the wavelet is inversely proportional to the scale a. One example of a characteristic frequency is the dominant frequency. Each scale of a particular wavelet may have a different characteristic frequency. The underlying mathematical detail required for the implementation within a time-scale can be found, for example, in Paul S. Addison, The Illustrated Wavelet Transform Handbook (Taylor & Francis Group 2002), which is hereby incorporated by reference herein in its entirety.
• The wavelet transform decomposes a signal using wavelets, which are generally highly localized in time. A continuous wavelet transform may provide a higher resolution relative to discrete transforms, thus providing the ability to garner more information from signals that typical frequency transforms such as Fourier transforms (or any other spectral techniques) or discrete wavelet transforms. Continuous wavelet transforms allow for the use of a range of wavelets with scales spanning the scales of interest of a signal such that small scale signal components correlate well with the smaller scale wavelets and thus manifest at high energies at smaller scales in the transform. Likewise, large scale signal components correlate well with the larger scale wavelets and thus manifest at high energies at larger scales in the transform. Thus, components at different scales may be separated and extracted in the wavelet transform domain. Moreover, the use of a continuous range of wavelets in scale and time position allows for a higher resolution transform than is possible relative to discrete techniques.
• In addition, transforms and operations that convert a signal or any other type of data into a spectral (i.e., frequency) domain create a series of frequency transform values in a two-dimensional coordinate system where the two dimensions may be frequency and, for example, amplitude. Wavelet transforms are further described in U.S. Pat. No. 7,944,551, entitled “Systems and Methods for a Wavelet Transform Viewer,” and U.S. Patent Application Publication No. 2010/0079279, entitled “Detecting a Signal Quality Decrease in a Measurement System,” both of which are hereby incorporated by reference in their entireties.
• Referring again to Equation (1), a modulus of the transform may be defined as |T(a,b)|. As such, the energy density function of the wavelet transform (that is, the scalogram) may be rescaled as follows:
• $\begin{array}{cc}{T\left(a,b\right)}^{*}=\frac{T\left(a,b\right)}{\sqrt{a}}& \mathrm{Equation}\left(2\right)\end{array}$
• The rescaled wavelet transform scalogram may be used to define ridges in wavelet space. The scalogram may be taken to include all suitable forms of rescaling including, but not limited to, the original unscaled wavelet representation, linear rescaling, any power of the modulus of the wavelet transform, or any other suitable rescaling. In addition, for purposes of clarity and conciseness, the term “scalogram” shall be taken to mean the wavelet transform, T(a,b) itself, or any part thereof. For example, the real part of the wavelet transform, the imaginary part of the wavelet transform, the phase of the wavelet transform, any other suitable part of the wavelet transform, or any combination thereof is intended to be conveyed by the term “scalogram”. A ridge is a locus of points of local maxima in a plane. Also, a ridge may be a path displaced from the locus of the local maxima. The rescaled wavelet transform scalogram allows for a representation in which ridges of bands on the transform surface scale directly with amplitudes of corresponding signal components. By using the rescaled transform, the direct scale relationship may take the simplified form as follows:
• 
  A_r=K·A_s  Equation (3)
• where A_ris the ridge amplitude, K is a constant, and A_sis the signal amplitude, which may be defined as the distance from peak-to-trough. For a sinusoidal signal, Equation (3) may take the form of the following:
• $\begin{array}{cc}{A}_r=\frac{\sqrt[4]{\mathrm{<figure-callout\; label="\pi "\; filenames="US20140213862A1-20140731-D00002.png"\; state="\{\{state\}\}">\pi </figure-callout>}}}{2}A_s& \mathrm{Equation}\left(4\right)\end{array}$
• Accordingly, the ridge amplitude may be related to the signal amplitude by a constant (K) of 0.67. That is, K={square root over (π)}/2. However, other constants may be experimentally or empirically derived, based on various factors, such as the type of wavelet transform being used.
• Wavelet transform features may be extracted from the wavelet decomposition of signals. For example, wavelet decomposition of physiological signals, such as PPG or blood pressure signals, may be used to provide clinically useful information.
• Pertinent repeating features in a signal give rise to a time-scale band in wavelet space or a resealed wavelet space. For example, the pulse component of a physiological pressure signal produces a dominant band in wavelet space at or around the pulse frequency.FIGS. 2(a) and2(b) illustrate two views of an illustrative scalogram derived from a PPG signal, according to an embodiment of the present disclosure.FIGS. 2(a) and2(b) show an example of the band caused by the pulse component in such a signal. The pulse band is located between the dashed lines in the plot ofFIG. 2(a). The pulse band is formed from a series of dominant coalescing features across the scalogram. The pulse band is more clearly seen as a raised band across the transform surface inFIG. 2(b) located within the region of scales indicated by the arrow in the plot. The maxima of the pulse band with respect to the scale is the ridge. The locus of the ridge is shown as a black curve on top of the band inFIG. 2(b). By employing a suitable rescaling of the scalogram, such as that given in equation (2), the ridges found in wavelet space may be related to the instantaneous frequency of the signal. In this way, the pulse rate may be obtained from the physiological signal, such as a PPG signal. Instead of rescaling the scalogram, a suitable predefined relationship between the scale obtained from the ridge on the wavelet surface and the actual pulse rate may also be used to determine the pulse rate.
• By mapping the time-scale coordinates of the pulse ridge onto the wavelet phase information gained through the wavelet transform, individual pulses may be captured. As such, both times between individual pulses and the timing of components within each pulse may be monitored and used to detect heart beat anomalies, measure arterial system compliance, or perform any other suitable calculations or diagnostics.
• FIG. 2(c) illustrates an exemplary scalogram derived from a signal containing two pertinent components, according to an embodiment of the present disclosure. As noted above, pertinent repeating features in the signal give rise to a time-scale band in wavelet space or a rescaled wavelet space. For a periodic signal, the band remains at a constant scale in the time-scale plane. For many real signals, especially biological signals, the band may be non-stationary—varying in scale, amplitude, or both over time. As shown inFIG. 2(c), the two pertinent components lead to two bands in the transform space. The bands are labeled band A and band B on the three-dimensional schematic of the wavelet surface. In an embodiment, the band ridge is defined as the locus of the peak values of the bands with respect to scale. For purposes of clarity, it may be assumed that band B contains the signal information of interest. As such, band B may be referred to as the “primary band.” In addition, it may be assumed that the system from which the signal originates, and from which the transform is subsequently derived, exhibits some form of coupling between the signal components in band A and band B. When noise or other erroneous features are present in the signal with similar spectral characteristics of the features of band B, then the information within band B can become ambiguous (for example, obscured, fragmented, or missing). As such, the ridge of band A may be followed in wavelet space and extracted either as an amplitude signal or a scale signal which may be referred to as the “ridge amplitude perturbation” (RAP) signal and the “ridge scale perturbation” (RSP) signal, respectively. The RAP and RSP signals may be extracted by projecting the ridge onto the time-amplitude or time-scale planes, respectively.
• FIG. 2(d) illustrates a schematic of signals associated with a ridge inFIG. 2(c), and a schematic of a further wavelet decomposition of the signals, according to an embodiment of the present disclosure. The top plots ofFIG. 2(d) illustrate a schematic of the RAP and RSP signals associated with ridge A inFIG. 2(c). Below the RAP and RSP signals are schematics of a further wavelet decomposition of the newly derived signals. The secondary wavelet decomposition allows for information in the region of band B inFIG. 2(c) to be made available as band C and band D. The ridges of bands C and D may serve as instantaneous time-scale characteristic measures of the signal components causing bands C and D. This technique, which may be referred to as secondary wavelet feature decoupling (SWFD), may allow information concerning the nature of the signal components associated with the underlying physical process causing the primary band B (shown inFIG. 2(c)) to be extracted when band B itself is obscured in the presence of noise or other erroneous signal features.
• FIG. 3 illustrates aMorlet wavelet300. As shown inFIG. 3, the Morlet wavelet is shown as a realvalue Morlet wavelet300. The Morlet wavelet300 is a wavelet that includes a complex exponential or carrier multiplied by a Gaussian window or envelope. The Morlet wavelet300 is generally related to human perception, such as hearing and vision. The Morlet wavelet300 may include aprimary peak302 that is preceded by a leadingpeak304, and followed by a trailingpeak306. Atrough308 is disposed between the leadingpeak304 and theprimary peak302, while atrough310 is disposed between theprimary peak302 and the trailingpeak306. Theprimary peak302 is typically greater in amplitude than the leading and trailingpeaks304 and306, respectively. Eachpeak302,304, and306 may be shaped as a generally smoothly fluctuating waveform.
• FIG. 4 illustrates aPPG signal400 over time, according to an embodiment of the present disclosure. The PPG signal400 is an example of a physiological signal detected by the physiologicalsignal detection module102. However, the physiological signal may be various other types of physiological signals, such as a blood pressure signal, an EKG signal, and the like.
• FIG. 5 illustrates a top plan view of ascalogram500 formed by theMorlet wavelet300 shown inFIG. 3 transforming the PPG signal400 shown inFIG. 4.FIG. 6 illustrates an isometric view of thescalogram500. Referring toFIGS. 3-6, the Morlet wavelet may be used as the wavelet function ψ(t), while the PPG signal is the signal x(t) in Equation (1). The Morlet wavelet300 transforms the PPG signal400 into thescalogram500. As such, components of the PPG signal400 are decoupled from one another and shown in thescalogram500. For example, thescalogram500 includes amain pulse band502 andrespiration components504. The physiologicalparameter determination module108 may analyze thescalogram500, including themain pulse band502 and therespiration components504 to determine one or more physiological parameters, such as pulse rate, blood pressure, and the like.
• However, while theMorlet wavelet300 includes a series of smooth, waveforms, the PPG signal400 may be irregular and include waveform portions that may be nominally sinusoidal and distinctly non-sinusoidal in shape. As such, the use of the Morlet wavelet300 to transform the PPG signal400 may result in ascalogram500 that may distort certain components of thePPG signal400. For example, certain components of the PPG signal400 may be misrepresented, over- or under-valued, or even missing from thescalogram500. Additionally, through use of theMorlet wavelet300, certain undesired aspects, such as noise, may be included in thescalogram500. Therefore, in using the Morlet wavelet300 to transform the PPG signal400 into thescalogram500, the physiological parameter determination module108 (shown inFIG. 1) may utilize additional processing and analysis in order to determine the physiological parameter(s).
• Accordingly, embodiments of the present disclosure provide a system and method of forming a wavelet that may be closely and directly correlated with a physiological signal, such as the PPG signal400, in order to provide more accurate and detailed scalograms.
• FIG. 7 illustrates asynthesized wavelet700, according to an embodiment of the present disclosure. Thesynthesized wavelet700 is similar to theMorlet wavelet300, except, instead of smooth, regular sinusoidal peaks, thesynthesized wavelet700 includespeaks702 havinginitial peak component704 separated from asecondary peak component706 by anotch708. Theinitial peak component704 and thesecondary peak component706 may generally be sinusoidal in shape. Thesecondary peak component706 may be superimposed, added, spliced, or the like onto theinitial peak component702 to form the double-hump shape with thenotch708 shown inFIG. 7. As compared to a Morlet wavelet, the double hump shape of thepeaks702 is more closely related to a PPG signal, which may include a primary peak separated from a trailing peak separate by a dichrotic notch. Accordingly, thesynthesized wavelet700 may be used as the wavelet function ψ(t) in Equation (1) to yield a more detailed and/or accurate scalogram. In general, the wavelet formation module104 (shown inFIG. 1) may use one or more shapes that are analogous or otherwise similar to portions of a generic shape of a physiological signal, such as a generic PPG signal, to form the synthesizedwavelet700.
• FIG. 8 illustrates a top plan view of ascalogram800 formed by the synthesizedwavelet700 shown inFIG. 7 transforming the PPG signal400 shown inFIG. 4, according to an embodiment of the present disclosure.FIG. 9 illustrates an isometric view of thescalogram800. Referring to FIGS.4 and7-9, thescalogram800 includes amain pulse band802,respiration components804, and an additionallower band806, which is not found in the scalogram500 (shown inFIGS. 5 and 6). Thelower band806 may represent noise or other interference that is decoupled from themain pulse band802. Additionally, thelower band806 may provide additional information that may be analyzed by the physiological parameter determination module108 (shown inFIG. 1). Thelower band806, by itself or in conjunction with themain pulse band802 and/or therespiration components804, may provide a more accurate approximation of thePPG signal400.
• While thesynthesized wavelet700 is described as a double-humped shape, various other mathematical functions and/or shapes may be used to better approximate the shape of thePPG signal400. In an example, a triple sinusoidal shape may be used. Additionally, other waveforms may be used in addition to, or in place of, the sinusoidal shapes.
• FIG. 10 illustrates asimplified PPG signal1000 over time, according to an embodiment of the present disclosure. ThePPG signal1000 is an example of a physiological signal. However, embodiments of the present disclosure may be used in relation to various other physiological signals, such as blood pressure signals, EKG signals, phonocardiogram signals, ultrasound signals, and the like. Referring toFIGS. 1 and 10, thePPG signal1000 may be shown as a waveform by a display, such as thedisplay110, which receives signal data from the physiologicalsignal detection module102.
• ThePPG signal1000 may include a plurality of pulses over a predetermined time period. The time period may be a fixed time period, or the time period may be variable. Moreover, the time period may be a rolling time period, such as a 1-60 second rolling timeframe.
• Each pulse may represent a single heartbeat and may include a pulse-transmitted orprimary peak1002 separated from a pulse-reflected or trailingpeak1004 by adichrotic notch1006. Theprimary peak1002 represents a pressure wave generated from the heart to the point of detection, such as in a finger, forehead, forearm, neck, or the like, where a pulse oximeter sensor, for example, is positioned. The trailingpeak1004 may represent a pressure wave that is reflected from the location proximate to where the pulse oximeter sensor is positioned back toward the heart.
• FIG. 11 illustrates a series of PPG signals1100 within a Gaussian window orenvelope1102, according to embodiment of the present disclosure. The PPG signals1100 may be part of a PPG signal trace that includes more or less PPG signals1100 than shown. Referring toFIGS. 1 and 11, thewavelet formation module104 may select a number of PPG signals1100 to be contained within the Gaussian window. Thewavelet formation module104 may be programmed to select a certain number of PPG signals1100 over a predetermined period of time, such as a current 1-60 second rolling timeframe. Alternatively, thewavelet formation module104 may contain only one PPG signal, such as thePPG signal1000 shown inFIG. 10, within the Gaussian window. Moreover, thewavelet formation module104 may change the sampling time over which theGaussian window1102 is plotted. For example, theGaussian window1102 may occur over changing periods of time, based on detected changes in physiological parameters of an individual.
• A natural period p of aPPG waveform1104 may be attributed to a wavelet scale a. That is, the period p may be equal to the wavelet scale a. In this manner, the period p of thePPG waveform1104 may be directly linked to the wavelet scale a. In one example, the period p of a cardiac pulsatile component (for example, the heart beat interval) of thePPG waveform1104 may equal the wavelet scale a. However, the period p and the wavelet scale a may be related in different ways. For example, the period p may be a fraction of the scale a, or vice versa. The Gaussian window orenvelope1102 may be represented by e^−t*2/2(where −t*2 is −t²) and has a standard deviation and confines thePPG waveform1104. By containing thePPG waveform1104 within theGaussian window1102, thewavelet formation module104 may form a wavelet that is based on the PPG signals1100. However, thewavelet formation module104 may form a wavelet based on the PPG signals1100 through other methods, such as through using the shape of aPPG signal1100 as one or more peaks of a wavelet, averagingmultiple PPG signals1100 over a certain period time, and using the averaged PPG signal as the shape of one or more peaks of a wavelet, substituting the shape of one ormore PPG signals1100 in place of the peaks of another wavelet, such as theMorlet wavelet300 shown inFIG. 3, and/or the like.
• In general, thewavelet formation module104 may form a wavelet that is based directly on a physiological signal, such as a PPG signal. The physiological signal may be continuously detected, and thewavelet formation module104 may adapt the formed wavelet based on changes in the PPG signal over time. Through directly analyzing the PPG signal, thewavelet transform module104 may shape the wavelet to directly correlate to, conform to, and/or resemble the PPG signal itself.
• FIG. 12 illustrates aPPG wavelet1200, according to an embodiment of the present disclosure. Referring toFIGS. 1 and 12, thewavelet transform module104 forms thewavelet1200 through a direct analysis of the PPG signal400, shown inFIG. 4. That is, thewavelet1200 is directly formed from and/or based on thePPG signal400. As shown, thewavelet1200 may include a plurality ofpeaks1202 that are not perfectly smooth and/or sinusoidal in nature. Because the PPG signal400 may be not a smooth, regular, and even signal, thewavelet1200 may also not be smooth, regular, and even. Instead, thewavelet1200 tracks closer to the actual morphology of thePPG signal400. Thewavelet1200 may includepeaks1202 havingsharp points1204,irregular curves1206, andlinear portions1208, which are directly correlated to the morphology of thePPG signal400. Accordingly, thewavelet1200 may be used with respect to Equation (1) to provide a scalogram that provides accurate, detailed information that is directly related to the actual PPG signal400 itself.
• FIG. 13 illustrates a top plan view of ascalogram1300 formed by thePPG wavelet1200 shown inFIG. 12 transforming the PPG signal400 shown inFIG. 4, according to an embodiment of the present disclosure.FIG. 14 illustrates an isometric view of thescalogram1300. Referring to FIGS.1 and13-14, amain pulse band1302 is separated fromcomponents1304. As an example, thecomponents1304 may represent additional information that has been separated from themain pulse band1302. As such, themain pulse band1302 is clearer, and may be more easily analyzed. Additionally,respiration components1306 may be reduced in the scalogram, as thecomponents1306 may not be desired for analysis. In short, by forming thewavelet transform1200 directly from the PPG signal400 itself, the resultingscalogram1300 is clearer and provides information, such as themain pulse band1302, that may be quickly and reliably analyzed. As an example, unwanted components, noise, and the like may be more clearly decoupled from themain pulse band1302. Thesystem100 may therefore provide accurate information regarding a desired physiological parameter, such as pulse rate.
• It has been found that thescalogram1300, which is formed through thewavelet1200 transforming the PPG signal400, provides a distinct reduction in respiration components and a distinct reduction in higher characteristic frequency components of the pulse wave. Because thewavelet1200 is morphology-specific (for example, directly based on the PPG signal400 itself), thewavelet1200 causes thescalogram1300 to suppress components that are not at the main pulse period. As such, thewavelet1200 may enhance themain pulse band1302, which may be used for a more accurate analysis and determination of a physiological parameter, such as pulse rate. As an example, thewavelet1200 may lead to a scalogram in which the amplitude of themain pulse band1302 is increased, while the amplitudes of thecomponents1304 and1306 are decreased.
• Alternatively, embodiments of the present disclosure may analyze thecomponents1304 and1306 to determine various other physiological parameters, such as respiration rate, blood pressure, and the like. By more clearly decoupling thecomponents1304 and1306 from themain pulse band1302, thesystem100 may quickly and readily determine various physiological parameters from thecomponents1304 and1306, and themain pulse band1302. Additionally, it has been found that using thewavelet1200, which is directly formed from the PPG signal400, for example, unwanted noise and distortions are filtered from thescalogram1300, thereby leading to more accurate determinations of physiological parameters.
• In an embodiment, the PPG signal400 may be band pass filtered before thewavelet1200 is used to transform the PPG signal400 into thescalogram1300. As an example, a band pass filter may be used to filter the PPG signal between 0.25 Hz and 10 Hz. In this manner, information from themain pulse band1302, for example, may be derived without resorting to higher frequency bands for analysis.
• FIG. 15 illustrates aPPG wavelet1500 derived from a derivative of the PPG signal400 shown inFIG. 4, according to an embodiment of the present disclosure. Embodiments of the present disclosure provide a wavelet that may be directly formed from thePPG signal400. However, as shown inFIG. 15, thewavelet1500 may also be derived from a derivative (for example, dx/dt, where x is the PPG signal and t is time) of thePPG signal400.
• As described above, the wavelet formation module104 (shown inFIG. 1), may be used to form a wavelet based on a physiological signal, such as thePPG signal400.
• FIG. 16 illustrates an isometric view of aPPG system1610, according to an embodiment of the present disclosure. ThePPG system1610 may be in communication with, or part of, thesystem100, shown inFIG. 1. ThePPG system1610 is an example of a detection sub-system that may be in communication with the physiological signal detection module102 (shown inFIG. 1). While thesystem1610 is shown and described as aPPG system1610, the system may be various other types of physiological detection systems, such as an electrocardiogram system, a phonocardiogram system, and the like. ThePPG system1610 may be a pulse oximetry system, for example. Thesystem1610 may include aPPG sensor1612 and aPPG monitor1614. ThePPG sensor1612 may include anemitter1616 configured to emit light into tissue of a patient. For example, theemitter1616 may be configured to emit light at two or more wavelengths into the tissue of the patient. ThePPG sensor1612 may also include spaced-apart photodetectors1618 that are configured to detect the emitted light from theemitter1616 that emanates from the tissue after passing through the tissue. Thephotodetectors1618 may be equidistant, but on opposite sides, from theemitter1616.
• Thesystem1610 may include a plurality of sensors forming a sensor array in place of thePPG sensor1612. Each of the sensors of the sensor array may be a complementary metal oxide semiconductor (CMOS) sensor, for example. Alternatively, each sensor of the array may be a charged coupled device (CCD) sensor. In another embodiment, the sensor array may include a combination of CMOS and CCD sensors. The CCD sensor may include a photoactive region and a transmission region configured to receive and transmit, while the CMOS sensor may include an integrated circuit having an array of pixel sensors. Each pixel may include a photodetector and an active amplifier.
• Theemitter1616 and thephotodetectors1618 may be configured to be located on opposite sides of a digit, such as a finger or toe, in which case the light that emanates from the tissue passes completely through the digit. Theemitter1616 and thephotodetectors1618 may be arranged so that light from theemitter1616 penetrates the tissue and is reflected by the tissue into thedetector1618, such as a sensor designed to obtain pulse oximetry data.
• Thesensor1612 or sensor array may be operatively connected to and draw power from themonitor1614, for example. Optionally, thesensor1612 may be wirelessly connected to themonitor1614 and include a battery or similar power supply (not shown). Themonitor1614 may be configured to calculate physiological parameters based at least in part on data received from thesensor1612 relating to light emission and detection. Alternatively, the calculations may be performed by and within thesensor1612 and the result of the oximetry reading may be passed to themonitor1614. Additionally, themonitor1614 may include adisplay1620 configured to display the physiological parameters or other information about thesystem1610. Themonitor1614 may also include aspeaker1622 configured to provide an audible sound that may be used in various other embodiments, such as for example, sounding an audible alarm in the event that physiological parameters are outside a predefined normal range.
• Thesensor1612, or the sensor array, may be communicatively coupled to themonitor1614 via acable1624. Alternatively, a wireless transmission device (not shown) or the like may be used instead of, or in addition to, thecable1624.
• Thesystem1610 may also include amulti-parameter workstation1626 operatively connected to themonitor1614. Theworkstation1626 may be or include acomputing sub-system1630, such as standard computer hardware. Thecomputing sub-system1630 may include one or more modules and control units, such as processing devices that may include one or more microprocessors, microcontrollers, integrated circuits, memory, such as read-only and/or random access memory, and the like. Theworkstation1626 may include adisplay1628, such as a cathode ray tube display, a flat panel display, such as a liquid crystal display (LCD), a light-emitting diode (LED) display, a plasma display, or any other type of monitor. Thecomputing sub-system1630 of theworkstation1626 may be configured to calculate physiological parameters and to show information from themonitor1614 and from other medical monitoring devices or systems (not shown) on thedisplay1628. For example, theworkstation1626 may be configured to display an estimate of a patient's blood oxygen saturation generated by the monitor1614 (referred to as an SpO₂measurement), pulse rate information from themonitor1614, and blood pressure from a blood pressure monitor (not shown) on thedisplay1628.
• Themonitor1614 may be communicatively coupled to theworkstation1626 via acable1632 and/or1634 that is coupled to a sensor input port or a digital communications port, respectively and/or may communicate wirelessly with theworkstation1626. Additionally, themonitor1614 and/orworkstation1626 may be coupled to a network to enable the sharing of information with servers or other workstations. Themonitor1614 may be powered by a battery or by a conventional power source such as a wall outlet.
• Thesystem1610 may also include afluid delivery device1636 that is configured to deliver fluid to a patient. Thefluid delivery device1636 may be an intravenous line, an infusion pump, any other suitable fluid delivery device, or any combination thereof that is configured to deliver fluid to a patient. The fluid delivered to a patient may be saline, plasma, blood, water, any other fluid suitable for delivery to a patient, or any combination thereof. Thefluid delivery device1636 may be configured to adjust the quantity or concentration of fluid delivered to a patient.
• Thefluid delivery device1636 may be communicatively coupled to themonitor1614 via acable1637 that is coupled to a digital communications port or may communicate wirelessly with theworkstation1626. Alternatively, or additionally, thefluid delivery device1636 may be communicatively coupled to theworkstation1626 via acable1638 that is coupled to a digital communications port or may communicate wirelessly with theworkstation1626.
• FIG. 17 illustrates a simplified block diagram of thePPG system1610, according to an embodiment of the present disclosure. When thePPG system1610 is a pulse oximetry system, theemitter1616 may be configured to emit at least two wavelengths of light (for example, red and infrared) intotissue1640 of a patient. Accordingly, theemitter1616 may include a red light-emitting light source such as a red light-emitting diode (LED)1644 and an infrared light-emitting light source such as aninfrared LED1646 for emitting light into thetissue1640 at the wavelengths used to calculate the patient's physiological parameters. For example, the red wavelength may be between about 600 nm and about 700 nm, and the infrared wavelength may be between about 800 nm and about 1000 nm. In embodiments where a sensor array is used in place of single sensor, each sensor may be configured to emit a single wavelength. For example, a first sensor may emit a red light while a second sensor may emit an infrared light.
• As discussed above, thePPG system1610 is described in terms of a pulse oximetry system. However, thePPG system1610 may be various other types of systems. For example, thePPG system1610 may be configured to emit more or less than two wavelengths of light into thetissue1640 of the patient. Further, thePPG system1610 may be configured to emit wavelengths of light other than red and infrared into thetissue1640. As used herein, the term “light” may refer to energy produced by radiative sources and may include one or more of ultrasound, radio, microwave, millimeter wave, infrared, visible, ultraviolet, gamma ray or X-ray electromagnetic radiation. The light may also include any wavelength within the radio, microwave, infrared, visible, ultraviolet, or X-ray spectra, and that any suitable wavelength of electromagnetic radiation may be used with thesystem1610. Thephotodetectors1618 may be configured to be specifically sensitive to the chosen targeted energy spectrum of theemitter1616.
• Thephotodetectors1618 may be configured to detect the intensity of light at the red and infrared wavelengths. Alternatively, each sensor in the array may be configured to detect an intensity of a single wavelength. In operation, light may enter thephotodetectors1618 after passing through thetissue1640. Thephotodetectors1618 may convert the intensity of the received light into electrical signals. The light intensity may be directly related to the absorbance and/or reflectance of light in thetissue1640. For example, when more light at a certain wavelength is absorbed or reflected, less light of that wavelength is received from the tissue by thephotodetectors1618. After converting the received light to an electrical signal, thephotodetectors1618 may send the signal to themonitor1614, which calculates physiological parameters based on the absorption of the red and infrared wavelengths in thetissue1640.
• In an embodiment, anencoder1642 may store information about thesensor1612, such as sensor type (for example, whether the sensor is intended for placement on a forehead or digit) and the wavelengths of light emitted by theemitter1616. The stored information may be used by themonitor1614 to select appropriate algorithms, lookup tables and/or calibration coefficients stored in themonitor1614 for calculating physiological parameters of a patient. Theencoder1642 may store or otherwise contain information specific to a patient, such as, for example, the patient's age, weight, diagnosis, and/or the like. The information may allow themonitor1614 to determine, for example, patient-specific threshold ranges related to the patient's physiological parameter measurements, and to enable or disable additional physiological parameter algorithms. Theencoder1642 may, for instance, be a coded resistor that stores values corresponding to the type ofsensor1612 or the types of each sensor in the sensor array, the wavelengths of light emitted byemitter1616 on each sensor of the sensor array, and/or the patient's characteristics. Optionally, theencoder1642 may include a memory in which one or more of the following may be stored for communication to the monitor1614: the type of thesensor1612, the wavelengths of light emitted byemitter1616, the particular wavelength each sensor in the sensor array is monitoring, a signal threshold for each sensor in the sensor array, any other suitable information, or any combination thereof.
• Signals from thephotodetectors1618 and theencoder1642 may be transmitted to themonitor1614. Themonitor1614 may include a general-purpose control unit, such as amicroprocessor1648 connected to aninternal bus1650. Themicroprocessor1648 may be configured to execute software, which may include an operating system and one or more applications, as part of performing the functions described herein. A read-only memory (ROM)1652, a random access memory (RAM)1654,user inputs1656, thedisplay1620, and thespeaker1622 may also be operatively connected to thebus1650.
• TheRAM1654 and theROM1652 are illustrated by way of example, and not limitation. Any suitable computer-readable media may be used in the system for data storage. Computer-readable media are configured to store information that may be interpreted by themicroprocessor1648. The information may be data or may take the form of computer-executable instructions, such as software applications, that cause the microprocessor to perform certain functions and/or computer-implemented methods. The computer-readable media may include computer storage media and communication media. The computer storage media may include volatile and non-volatile media, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. The computer storage media may include, but are not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store desired information and that may be accessed by components of the system.
• Themonitor1614 may also include a time processing unit (TPU)1658 configured to provide timing control signals to alight drive circuitry1660, which may control when theemitter1616 is illuminated and multiplexed timing for thered LED1644 and theinfrared LED1646. TheTPU1658 may also control the gating-in of signals from thephotodetectors1618 through anamplifier1662 and aswitching circuit1664. The signals are sampled at the proper time, depending upon which light source is illuminated. The received signals from thephotodetectors1618 may be passed through anamplifier1666, alow pass filter1668, and an analog-to-digital converter1670. The digital data may then be stored in a queued serial module (QSM)1672 (or buffer) for later downloading to RAM1654 asQSM1672 fills up. In an embodiment, there may be multiple separate parallelpaths having amplifier1666,filter1668, and A/D converter1670 for multiple light wavelengths or spectra received.
• Themicroprocessor1648 may be configured to determine the patient's physiological parameters, such as SpO₂and pulse rate using various algorithms and/or look-up tables based on the value(s) of the received signals and/or data corresponding to the light received by thephotodetectors1618. The signals corresponding to information about a patient, and regarding the intensity of light emanating from thetissue1640 over time, may be transmitted from theencoder1642 to adecoder1674. The transmitted signals may include, for example, encoded information relating to patient characteristics. Thedecoder1674 may translate the signals to enable themicroprocessor1648 to determine the thresholds based on algorithms or look-up tables stored in theROM1652. Theuser inputs1656 may be used to enter information about the patient, such as age, weight, height, diagnosis, medications, treatments, and so forth. Thedisplay1620 may show a list of values that may generally apply to the patient, such as, for example, age ranges or medication families, which the user may select using theuser inputs1656.
• Thefluid delivery device1636 may be communicatively coupled to themonitor1614. Themicroprocessor1648 may determine the patient's physiological parameters, such as a change or level of fluid responsiveness, and display the parameters on thedisplay1620. In an embodiment, the parameters determined by themicroprocessor1648 or otherwise by themonitor1614 may be used to adjust the fluid delivered to the patient viafluid delivery device1636.
• As noted, thePPG system1610 may be a pulse oximetry system. A pulse oximeter is a medical device that may determine oxygen saturation of blood. The pulse oximeter may indirectly measure the oxygen saturation of a patient's blood (as opposed to measuring oxygen saturation directly by analyzing a blood sample taken from the patient) and changes in blood volume in the skin. Ancillary to the blood oxygen saturation measurement, pulse oximeters may also be used to measure the pulse rate of a patient. Pulse oximeters measure and display various blood flow characteristics including, but not limited to, the oxygen saturation of hemoglobin in arterial blood.
• A pulse oximeter may include a light sensor, similar to thesensor1612, that is placed at a site on a patient, typically a fingertip, toe, forehead or earlobe, or in the case of a neonate, across a foot. The pulse oximeter may pass light using a light source through blood perfused tissue and photoelectrically sense the absorption of light in the tissue. For example, the pulse oximeter may measure the intensity of light that is received at the light sensor as a function of time. A signal representing light intensity versus time or a mathematical manipulation of this signal (for example, a scaled version thereof, a log taken thereof, a scaled version of a log taken thereof, and/or the like) may be referred to as the PPG signal. In addition, the term “PPG signal,” as used herein, may also refer to an absorption signal (for example, representing the amount of light absorbed by the tissue) or any suitable mathematical manipulation thereof. The light intensity or the amount of light absorbed may then be used to calculate the amount of the blood constituent (for example, oxyhemoglobin) being measured as well as the pulse rate and when each individual pulse occurs.
• The light passed through the tissue is selected to be of one or more wavelengths that are absorbed by the blood in an amount representative of the amount of the blood constituent present in the blood. The amount of light passed through the tissue varies in accordance with the changing amount of blood constituent in the tissue and the related light absorption. Red and infrared wavelengths may be used because it has been observed that highly oxygenated blood will absorb relatively less red light and more infrared light than blood with lower oxygen saturation. By comparing the intensities of two wavelengths at different points in the pulse cycle, it is possible to estimate the blood oxygen saturation of hemoglobin in arterial blood.
• ThePPG system1610 and pulse oximetry may be further described in United States Patent Application Publication No. 2012/0053433, entitled “System and Method to Determine SpO₂Variability and Additional Physiological Parameters to Detect Patient Status,” United States Patent Application Publication No. 2010/0324827, entitled “Fluid Responsiveness Measure,” and United States Patent Application Publication No. 2009/0326353, entitled “Processing and Detecting Baseline Changes in Signals,” all of which are hereby incorporated by reference in their entireties.
• It will be understood that the present disclosure is applicable to any suitable physiological signals and that PPG are used for illustrative purposes. Those skilled in the art will recognize that the present disclosure has wide applicability to other signals including, but not limited to other physiological signals (for example, electrocardiogram, electroencephalogram, electrogastrogram, electromyogram, heart rate signals, pathological sounds, ultrasound, or any other suitable biosignal) and/or any other suitable signal, and/or any combination thereof. Thus, the wavelet formation module104 (shown inFIG. 1) may be used to form a wavelet based on various other physiological signals, such as a blood pressure signal, an EKG signal, and the like.
• FIG. 18 illustrates ablood pressure signal1800 over time, according to an embodiment of the present disclosure. Blood pressure represents a measurement that quantifies a pressure exerted by circulating blood upon walls of blood vessels. In general, blood pressure is an example of a principal vital sign. Typically, blood pressure may be measured through use of a sphygmomanometer, or blood pressure cuff, and a stethoscope. However, blood pressure may also be invasively detected through an arterial line catheter, for example.
• Theblood pressure signal1800 is an example of a physiological signal that may be detected by a detection sub-system, such as a blood pressure detection sub-system. As shown, an amplitude of theblood pressure signal1800, may vary over time. For example, the amplitude may vary with respect to a base, average, or mean blood pressure of 120 systolic over 80 diastolic. As an example, the amplitude may change fromblood pressure pulse1802 toblood pressure pulse1804. The physiological signal detection module102 (shown inFIG. 1) and/or a pressure detection sub-system, may track the change in amplitude of theblood pressure signal1800, and store the change in amplitude for analysis. The wavelet formation module104 (shown inFIG. 1) may form a wavelet based on theblood pressure signal1800, for example. In an embodiment, thewavelet formation module104 may contain theblood pressure signal1800 within a Gaussian window, as described above with respect toFIG. 11, to form the wavelet.
• FIG. 19 illustrates anEKG signal1900, according to an embodiment of the present disclosure. Electrocardiography represents a transthoracic (across the thorax or chest) measurement of electrical activity of the heart over a period of time, as detected by electrodes attached to the outer surface of the skin and recorded by a device external to the body. The recording produced by the noninvasive procedure is termed an EKG or ECG. An EKG is used to measure the rate and regularity of heartbeats, as well as the size and position of the chambers, the presence of any damage to the heart, and the effects of drugs or devices used to regulate the heart, such as a pacemaker.
• TheEKG signal1900 is an example of a physiological signal. TheEKG signal1900 includes a P-wave1902, a QRS complex1904, and aT wave1906. TheP wave1902 indicates atrial depolarization, or contraction of the atrium. TheQRS complex1904 indicates ventricular depolarization, or contraction of the ventricles. TheT wave1906 indicates ventricular repolarization.
• The wavelet formation module104 (shown inFIG. 1) may form a wavelet based on theEKG signal1900, for example. In an embodiment, thewavelet formation module104 may contain theEKG signal1900 within a Gaussian window, as described above with respect toFIG. 11, to form the wavelet.
• While physiological signals, such as PPG signals, blood pressure signals, and EKG signals are shown and described, it is understood that embodiments of the present disclosure may be used with respect to various other physiological signals.
• FIG. 20 illustrates a flow chart of a method of determining one or more physiological parameters, according to an embodiment of the present disclosure. The process begins at2000, in which a physiological signal, such as any of those described above, is detected. Next, at2002, a wavelet based on the physiological signal is formed. The wavelet may be directly based on the physiological signal, such as through one or more portions or traces of the physiological being analyzed to form the wavelet having an analogous or similar shape to the physiological signal. Alternatively, the wavelet may be synthesized, such as through shapes, forms, and contours that are analogous or otherwise similar to a shape of a normal physiological signal, for example.
• Then, at2004, the wavelet is applied to the physiological signal to transform the physiological signal into a scalogram. Accordingly, the scalogram is highly correlated to the physiological signal, as the transforming wavelet is based on the physiological signal (in contrast to a generalized waveform that is not related to the physiological signal). Then, at2006, one or more physiological parameters are determined through an analysis of one or more components of the scalogram. It has been found that a scalogram formed by the physiological signal-based wavelet operating on the physiological signal provides a scalogram with distinctly decoupled component parts, such as a pulse band and respiratory components, that may be readily and accurately analyzed.
• After2006, the process returns to2000. Accordingly, the wavelet may be continually adapted and reformed based on the current state of a physiological signal. As such, the embodiments of the present disclosure provide a system and method that may continually adapt to changing physiological conditions of an individual that is being monitored through one or more detection sub-systems.
• Embodiments of the present disclosure provide a system and method that forms a wavelet that may be described by a real function. That is, the wavelet may include no complex parts. Alternatively, the wavelet may be described by real and complex, including imaginary, components. For example, in an embodiment, a negative part of a spectrum of a wavelet may be removed, thereby producing a complex wavelet that may be analyzed. Such an embodiment may be particularly useful when analyzing non-stationary signals with changing component features and variable natural periods.
• Embodiments of the present disclosure may be used to determine physiological parameters, such as pulse rate. Additionally, embodiments of the present disclosure may be used to determine various other physiological parameters, such as respiration rate, respiration effort, changes in blood pressure, cardiac output, vasoconstriction, awareness and arousal, and the like.
• Embodiments of the present disclosure provide a system and method of forming a wavelet that may be based on a physiological signal itself. Alternatively, the wavelet may be based on a shape that conforms to a shape of a general physiological signal. Because the wavelet is based on the physiological signal itself (whether through direct analysis of the PPG signal and formation of the wavelet based on the direct analysis of the PPG signal, or through a shape that correlates to a general shape of the physiological signal), the resulting scalogram provides clear, reliable, accurate, and useful information that may be used to determine one or more physiological parameters.
• Various embodiments described herein provide a tangible and non-transitory (for example, not an electric signal) machine-readable medium or media having instructions recorded thereon for a processor or computer to operate a system to perform one or more embodiments of methods described herein. The medium or media may be any type of CD-ROM, DVD, floppy disk, hard disk, optical disk, flash RAM drive, or other type of computer-readable medium or a combination thereof.
• The various embodiments and/or components, for example, the control units, modules, or components and controllers therein, also may be implemented as part of one or more computers or processors. The computer or processor may include a computing device, an input device, a display unit and an interface, for example, for accessing the Internet. The computer or processor may include a microprocessor. The microprocessor may be connected to a communication bus. The computer or processor may also include a memory. The memory may include Random Access Memory (RAM) and Read Only Memory (ROM). The computer or processor may also include a storage device, which may be a hard disk drive or a removable storage drive such as a floppy disk drive, optical disk drive, and the like. The storage device may also be other similar means for loading computer programs or other instructions into the computer or processor.
• As used herein, the term “computer” or “module” may include any processor-based or microprocessor-based system including systems using microcontrollers, reduced instruction set computers (RISC), application specific integrated circuits (ASICs), logic circuits, and any other circuit or processor capable of executing the functions described herein. The above examples are exemplary only, and are thus not intended to limit in any way the definition and/or meaning of the term “computer” or “module.”
• The computer or processor executes a set of instructions that are stored in one or more storage elements, in order to process input data. The storage elements may also store data or other information as desired or needed. The storage element may be in the form of an information source or a physical memory element within a processing machine.
• The set of instructions may include various commands that instruct the computer or processor as a processing machine to perform specific operations such as the methods and processes of the various embodiments of the subject matter described herein. The set of instructions may be in the form of a software program. The software may be in various forms such as system software or application software. Further, the software may be in the form of a collection of separate programs or modules, a program module within a larger program or a portion of a program module. The software also may include modular programming in the form of object-oriented programming. The processing of input data by the processing machine may be in response to user commands, or in response to results of previous processing, or in response to a request made by another processing machine.
• As used herein, the terms “software” and “firmware” are interchangeable, and include any computer program stored in memory for execution by a computer, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are exemplary only, and are thus not limiting as to the types of memory usable for storage of a computer program.
• While various spatial and directional terms, such as top, bottom, lower, mid, lateral, horizontal, vertical, front, and the like may be used to describe embodiments, it is understood that such terms are merely used with respect to the orientations shown in the drawings. The orientations may be inverted, rotated, or otherwise changed, such that an upper portion is a lower portion, and vice versa, horizontal becomes vertical, and the like.
• It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings without departing from its scope. While the dimensions, types of materials, and the like described herein are intended to define the parameters of the disclosure, they are by no means limiting and are exemplary embodiments. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.” Moreover, in the following claims, the terms “first,” “second,” and “third,” etc. are used merely as labels, and are not intended to impose numerical requirements on their objects. Further, the limitations of the following claims are not written in means—plus-function format and are not intended to be interpreted based on 35U.S.C. §112, sixth paragraph, unless and until such claim limitations expressly use the phrase “means for” followed by a statement of function void of further structure.

Claims (20)

What is claimed is:

1. A system for analyzing a physiological signal detected from an individual, the system comprising:

a physiological signal detection module configured to detect the physiological signal of the individual;

a wavelet formation module configured to form a wavelet based on the physiological signal; and

a wavelet transform module configured to generate a scalogram by transforming the physiological signal with the wavelet based on the physiological signal.

2. The system ofclaim 1, further comprising a physiological parameter determination module configured to determine one or more physiological parameters of the individual through an analysis of the scalogram.

3. The system ofclaim 1, further comprising a detection sub-system in communication with the physiological signal detection module, wherein the physiological signal detection module is configured to receive the physiological signal from the detection sub-system.

4. The system ofclaim 3, wherein the detection sub-system comprises a photoplethysmograph (PPG) sub-system, and wherein the physiological signal comprises a PPG signal.

5. The system ofclaim 3, wherein the detection sub-system comprises a blood pressure detection sub-system, and wherein the physiological signal comprises a blood pressure signal.

6. The system ofclaim 3, wherein the detection sub-system comprises an electrocardiogram (EKG) sub-system, and wherein the physiological signal comprises an EKG signal.

7. The system ofclaim 1, wherein the wavelet formation module is configured to form the wavelet by directly analyzing the physiological signal and shaping the wavelet to directly correlate to the physiological signal.

8. The system ofclaim 7, wherein the wavelet formation module is configured to contain at least a portion of the physiological signal within a Gaussian window to form the wavelet.

9. The system ofclaim 1, wherein the wavelet is a derivative of the physiological signal.

10. The system ofclaim 1, wherein the wavelet formation module is configured to form the wavelet by using one or more shapes that are similar to one or more portions of a generic shape of the physiological signal.

11. A method of analyzing a physiological signal detected from an individual, the method comprising:

detecting a physiological signal of the individual with a physiological signal detection module;

forming a wavelet based on the physiological signal with a wavelet formation module; and

generating a scalogram with a wavelet transform module by transforming the physiological signal with the wavelet based on the physiological signal.

12. The method ofclaim 11, wherein the physiological signal comprises one or more of a photoplethysmograph (PPG) signal, a blood pressure signal, or an electrocardiogram (EKG) signal.

13. The method ofclaim 11, wherein the forming operation comprises directly analyzing the physiological signal and shaping the wavelet to directly correlate to the physiological signal.

14. The method ofclaim 13, wherein the forming operation comprises containing at least a portion of the physiological signal within a Gaussian window to form the wavelet.

15. The method ofclaim 13, wherein the forming operation comprises using one or more shapes that are similar to one or more portions of a generic shape of the physiological signal.

16. A tangible and non-transitory computer readable medium that includes one or more sets of instructions configured to direct a computer to:

detect a physiological signal of an individual;

form a wavelet based on the physiological signal; and

generate a scalogram by transforming the physiological signal with the wavelet based on the physiological signal.

17. The tangible and non-transitory computer readable medium ofclaim 16, wherein the physiological signal comprises one or more of a photoplethysmograph (PPG) signal, a blood pressure signal, or an electrocardiogram (EKG) signal.

18. The tangible and non-transitory computer readable medium ofclaim 16, wherein the one or more instructions are further configured to direct the computer to directly analyze the physiological signal and shape the wavelet to directly correlate to the physiological signal.

19. The tangible and non-transitory computer readable medium ofclaim 18, wherein the one or more instructions are further configured to direct the computer to contain at least a portion of the physiological signal within a Gaussian window.

20. The tangible and non-transitory computer readable medium ofclaim 16, wherein the one or more instructions are further configured to direct the computer to use one or more shapes that are similar to one or more portions of a generic shape of the physiological signal to form the wavelet.

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